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Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions.

A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation).

Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN.

The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities.

The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

This paper aims to first analyze the reasons of a brick-wooden building tilts in Yunnan province in China. Then, a jacking method which combines the stress release methods and the excavation of stress release trench were put forward.

This method uses the anchor static pile to guarantee the safety of the building in the process of rectifying.

The practice showed that the inclination rate of the building was stable in the process of jacking, which proved the efficiency of the proposed method.

It has a great value for similar rectification projects, and for the protection of cultural heritage and ancient buildings have a positive effect.

This study aims to investigate whether lending to women decreases sustainability of microfinance institutions (MFIs) and how regional characteristics where MFIs are located moderate this effect.

Financial and operating data of MFIs and national cultures are available from the MIX Market database and the Hofstede’s publications. These data are analyzed by using multiple regression models with the financial self-sustainability, proportion of women borrowers in the MFI’s lending portfolio, and dimensions of national culture as dependent, explanatory and moderating variables.

Lending to women tends to reduce sustainability of MFIs. This negative effect is more pronounced in countries ranking higher on power distance and individualism, but the effect is less serious in countries ranking higher on masculinity and uncertainty avoidance.

Many studies demonstrate that MFIs improve their repayment rates by targeting women borrowers. The increase in repayment rates, however, may not always improve their sustainability. Further, as microfinance industry increasingly diversifies geographically, regional characteristics where MFIs are located play a vital contingent role in their sustainability.

Impressions about product quality and reliability can depend as much on perceptions about brands and country of origin as on data regarding performance and failure. This has implications for companies in developing countries that need to compete with importers. For manufacturers in industrialised countries it has implications for the value of transferred technologies. This article considers the issue of quality and reliability when technology is transferred between countries with different levels of development. It is based on UK and Chinese company case studies and questionnaire surveys undertaken among three company groups: UK manufacturers; Chinese manufacturers; Chinese users. Results show that all three groups recognise quality and reliability as important and support the premise that foreign technology based machines made in China carry a price premium over Chinese machines based on local technology. Closer examination reveals a number of important differences concerning the perceptions and reality of quality and reliability between the groups.

The value of technology and the appropriate form of transfer arrangement are important questions to be resolved when transferring technology between Western manufacturing firms and partners in industrialising and developing countries. This article reports on surveys carried out in the machine tool industries in the UK and China to establish the differences and similarities between owners and acquirers of technology regarding the relative importance of the factors they evaluate, and the assessments they make, when considering a technology transfer. It also outlines the development of a framework for technology valuation. The survey results indicate that the value of product technology is related to superior technical performance, especially on reliability and functionality, and the prospects of premium prices and increased sales of the technology transfer based machine tools. Access to markets is the main objective of UK companies, while Chinese companies are concerned about improving their technological capability. There are significant risks, especially related to performance in the market, and while owners and acquirers have benefited in the short term, the long term collaboration required for strategic benefits has been difficult to achieve because of the different priorities of the owners and the acquirers.

Results of complementary surveys of foreign and Chinese manufacturing enterprises with respect to their objectives and expectations regarding technology transfer into China show that the major strategic objective of foreign enterprises, to gain access to the Chinese market, fits well with Chinese enterprises’ main objective of improving domestic competitiveness but less well with that of accessing world markets through technology transfer. Foreign firms rate highly the capability of Chinese enterprises to learn new technologies and also find the Chinese macro environment for business favourable. The survey results provide information that will help managers with their negotiations on co‐operating with prospective partners for the transfer of technology as well as assisting policy makers who wish to facilitate more effective transfer arrangements.

The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.

A series of six‐hour static tests have been implemented at room temperature, and the static measurements have been collected from MEMS IMU. In order to characterize the various types of random noise terms for the IMU, the basic definition and main procedure of the Allan variance method are investigated. Unlike the normal Allan variance method, which has the shortcomings of processing large data sets and requiring long computation time, a modified Allan variance method is proposed based on the features of data distribution in the log‐log plot of the Allan standard deviation versus the averaging time.

Experiment results demonstrate that the modified Allan variance method can effectively estimate the noise coefficients for MEMS IMU, with controllable computation time and acceptable estimation accuracy.

This paper proposes a time‐controllable Allan variance method which can quickly and accurately identify different noise terms imposed by the stochastic fluctuations.

The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system.

The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data.

Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation.

Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints.

The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor.

This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

Variable fractional delay filtering is an important technology in signal processing; the research shows that all-pass variable fractional delay (VFD) filters achieve higher design accuracy than FIR VFD filters; therefore, the design, analysis and implementation of all-pass VFD filters are of great importance.

In this paper, a two-stage approach for the design of general 1-D stable VFD all-pass filters is proposed. The method takes the desired group delay range [N−1, N], where N is the filter order.

The design algorithm is decomposed into two design stages: first, a set of fixed delay all-pass filters are designed by minimizing a set of objective functions defined in terms of approximating error criterion and filter stability constraint. Then, the design result is determined by fitting each of the fixed delay all-pass filter coefficients as 1-D polynomials. A design example together with its comparisons with those of the recent literature studies is given to justify the effectiveness of the proposed design method.

An illustrating design example shows that the method proposed can achieve better filter performances than the existing ones.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.